Online transactions are being used more and more for purchases of goods and services. Such online transactions are typically conducted over a computer network such as the Internet, where access is gained either through a computer platform, such as a desktop computer or laptop computer, or through a mobile platform, such as a smart phone that access the Internet through a telecommunication carrier associated with the smart phone or through a “WiFi” connection. Recommendations systems, which are becoming increasingly common in online sales and digital media sites, typically apply information about a user's preferences and purchasing behavior to recommend content, goods, or services in which the user may be interested. Such recommendations systems usually provide a means of content discovery, where the user is presented with relevant content without the user having to explicitly request or look for the content. In this way, goods and services of which the user may have been unaware, may be brought to the attention of the user, for purchase. In addition, the user may be kept abreast of special offers and price reductions of interest. The recommendations systems thereby provide a service to the user and can help increase sales for providers.
In general, the computations and data processing that are typically used to generate recommendations are well-known and well-documented, and are described in publicly available literature. The challenges facing recommendations systems include processing the large volumes of data that are utilized in determining recommendations and ensuring data privacy of users in making the recommendation determinations. In addition, the delivery of recommendation services in the mobile platform context can involve unique challenges, given that network access via a mobile platform such as a smart phone may be limited to a proprietary network associated with the smart phone service provider. Another challenge, in the mobile platform arena, is to provide recommendations that are compatible with an individual destination device and platform. Also, in the mobile context, users may engage in data communications and other activity in multiple networks, both in-network and out-of-network as compared to the network of the user's service provider.
The present invention addresses these concerns for delivery of recommendations to users over a computer network and provides efficient, relevant recommendations in response to activity by multiple users on multiple networks while maintaining privacy and integrity of user data.